trying URL 'http://cran.us.r-project.org/bin/macosx/contrib/4.0/maps_3.3.0.tgz'
Content type 'application/x-gzip' length 3687860 bytes (3.5 MB)
==================================================
downloaded 3.5 MB

The downloaded binary packages are in
    /var/folders/gs/080wb67j58df5y8ccjf53yw00000gn/T//RtmpKGeCND/downloaded_packages
trying URL 'http://cran.us.r-project.org/bin/macosx/contrib/4.0/tmap_3.3-1.tgz'
Content type 'application/x-gzip' length 3585561 bytes (3.4 MB)
==================================================
downloaded 3.4 MB

The downloaded binary packages are in
    /var/folders/gs/080wb67j58df5y8ccjf53yw00000gn/T//RtmpKGeCND/downloaded_packages
trying URL 'http://cran.us.r-project.org/bin/macosx/contrib/4.0/rgeos_0.5-5.tgz'
Content type 'application/x-gzip' length 8537441 bytes (8.1 MB)
==================================================
downloaded 8.1 MB

The downloaded binary packages are in
    /var/folders/gs/080wb67j58df5y8ccjf53yw00000gn/T//RtmpKGeCND/downloaded_packages
    ADM_RATE < 0.2 ~ 'elite/highly selective',
Error: unexpected ',' in "    ADM_RATE < 0.2 ~ 'elite/highly selective',"

Simple Scattergram

First we are going to try to present this pattern for different tiers of universities (admission rate as well as debt)

This is a data table showing the breakdown of the university ranks (as I chose to rank them). I will note that from the dt alone wwe are seeing a downward trend in the ‘Median Student Loans’ column.

# Create Data Table (Summarized) for 
library('scales')

sc_dt <- sc %>% subset(DEBT_MDN !='PrivacySuppressed') %>% transform(DEBT_MDN = as.numeric(DEBT_MDN)) %>% group_by(uni_rank) %>% mutate(`Number of Universities` = n()) %>% ungroup() %>% mutate(DEBT_MDN_STUDENTS = DEBT_MDN*UGDS) %>% group_by(uni_rank) %>% mutate(`Median Student Loans` = paste('$',round(sum(DEBT_MDN_STUDENTS, na.rm=TRUE)/sum(UGDS, na.rm=TRUE),2))) %>% 
  mutate(`Min Acceptance Rate` = percent(min(ADM_RATE))) %>% mutate(`Max Acceptance Rate` = percent(max(ADM_RATE))) %>% ungroup() %>% 
  group_by(uni_rank,`Median Student Loans`,`Number of Universities`,`Min Acceptance Rate`,`Max Acceptance Rate`) %>% 
  summarize()
`summarise()` regrouping output by 'uni_rank', 'Median Student Loans', 'Number of Universities', 'Min Acceptance Rate' (override with `.groups` argument)
install.packages('DT')
Error in install.packages : Updating loaded packages
library(DT)
table <- datatable(sc_dt,style = "default",filter = 'top',  caption = 'Universities and Selectivity')
table
NA

Scattergram as Violin Plot

Showing the previous scattergram specifically as violing plots

ipeds15 <- get_education_data(level = "college-university",
    source = "ipeds",
    topic = "grad-rates-pell",
    filters = list(year = 2015))
ipeds15

Student Debt over Time

#Process the data 
sc_time <- read_csv('2010_2019_student_debt.csv') 
Error: '2010_2019_student_debt.csv' does not exist in current working directory ('/Users/ConnieXu/Dropbox (Business)/Spring 2021/QMSS 5063 - Data Visualization /Group_G_HigherEd/ideas_drafts').
sum(sc_time$UGDS,na.rm=TRUE)
[1] 90801007

Below is an interactive line graph (2010-2019) which details the trends in student debt over the years.

# CPI Inflation Rates - Got Average Yearly Inflation Rate for Scaling for Student Debt 
install.packages('quantmod')
Error in install.packages : Updating loaded packages
library(quantmod)
getSymbols("CPIAUCSL", src='FRED')
[1] "CPIAUCSL"
avg.cpi <- apply.yearly(CPIAUCSL, mean)
cf <- as.data.frame(avg.cpi/as.numeric(avg.cpi['2009'])) 
cf$Year_Ending <- format(as.Date(row.names(cf), format="%Y-%m-%d"),"%Y")

# Merged for Inflation 
sc_time_df <- sc_time %>% group_by(`Year_Ending`) %>% mutate(`Average Annual Student Debt - National` = sum(DEBT_MDN_STUDENT,na.rm=TRUE)/sum(UGDS,na.rm=TRUE)) %>% ungroup() %>% 
  dplyr::mutate(uni_rank = case_when(
    ADM_RATE < 0.2 ~ 'elite/highly selective',
    ADM_RATE < 0.3 ~ 'more selective',
    ADM_RATE < 0.5 ~ 'selective',
    ADM_RATE < 0.7 ~ 'less selective',
    TRUE ~ 'not selective')) %>%
  mutate(uni_rank = factor(uni_rank, levels=c('not selective', 'less selective', 'selective', 
                                              'more selective', 'elite/highly selective'))) %>%
  group_by(uni_rank,Year_Ending) %>% 
  mutate(`Average Annual Student Debt (by Selectivity)` = sum(DEBT_MDN_STUDENT,na.rm=TRUE)/sum(UGDS,na.rm=TRUE)) %>% 
  ungroup() %>% 
  group_by(`Year_Ending`,`Average Annual Student Debt (by Selectivity)`,
           uni_rank,`Average Annual Student Debt - National`) %>% summarize() %>% 
  merge(cf) %>% 
  mutate(`Adjusted Average Annual Student Debt` = `Average Annual Student Debt (by Selectivity)`/
           CPIAUCSL) %>% 
  mutate(`Adjusted Average Annual Student Debt - Composite` = `Average Annual Student Debt - National`/
           CPIAUCSL)
`summarise()` regrouping output by 'Year_Ending', 'Average Annual Student Debt (by Selectivity)', 'uni_rank' (override with `.groups` argument)
sc_df <- sc_time_df %>% group_by(`Average Annual Student Debt - National`,`Adjusted Average Annual Student Debt - Composite`,Year_Ending) %>% summarize() %>% mutate(uni_rank='national average') %>% mutate(`Adjusted Average Annual Student Debt`=`Adjusted Average Annual Student Debt - Composite`) %>% dplyr::mutate(`Average Annual Student Debt (by Selectivity)` = `Average Annual Student Debt - National`) %>% merge(cf) %>% select(Year_Ending,`Average Annual Student Debt (by Selectivity)`, uni_rank, `Average Annual Student Debt - National`, CPIAUCSL, `Adjusted Average Annual Student Debt`,`Adjusted Average Annual Student Debt - Composite`)
`summarise()` regrouping output by 'Average Annual Student Debt - National', 'Adjusted Average Annual Student Debt - Composite' (override with `.groups` argument)
sc_time_df <- sc_time_df %>% rbind(sc_df) %>% mutate(uni_rank = factor(uni_rank, levels=c('national average','not selective', 'less selective', 'selective', 'more selective', 'elite/highly selective'))) %>% 
  mutate(national = ifelse(uni_rank == 'national average', 'y','n'))
sc_df
sc_time_df
brewer.pal(n=10,"PuBuGn")
n too large, allowed maximum for palette PuBuGn is 9
Returning the palette you asked for with that many colors
[1] "#FFF7FB" "#ECE2F0" "#D0D1E6" "#A6BDDB" "#67A9CF" "#3690C0" "#02818A" "#016C59" "#014636"
ShortPuBuGn <- c("#D0D1E6","#A6BDDB","#67A9CF","#3690C0","#02818A")

p <- sc_time_df %>% 
  ggplot(.,aes(x=Year_Ending,y=`Adjusted Average Annual Student Debt`, color=uni_rank, group=national)) + 
  geom_point() + geom_line(aes(linetype=national)) + 
  scale_color_manual(values=c('grey',"#D0D1E6","#A6BDDB","#67A9CF","#3690C0","#02818A"))+
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.background= element_rect(fill="white")) +
  scale_x_continuous(breaks = round(seq(min(sc_time$Year_Ending), max(sc_time$Year_Ending), by = 2),1)) +
  labs(x='', y='Median Loan Amount per Student\n(thousands)**', 
       title='Student Debt Has Been Rising Over The Years',
       color='',fill='', caption='**-inflation adjusted')
ggplotly(p)
NA
NA

The following is (instead) a bar graph with fewer ‘university selectivity’ buckets.

Chloropleth showing average student debt (this is only 2019 but I have a filter at the beginning that will allow me to incorporate a slider for the year).

leaflet(states_2019) %>% addProviderTiles("CartoDB.Positron") %>%
  addPolygons(fillColor = ~pal(states_2019$`Average Student Loans`),
              color = "white",
              weight = 0.5,
              fillOpacity = 0.7,  
              highlight = highlightOptions(
                weight = 5,
                color = "#666",
                fillOpacity = 0.7,
                bringToFront = TRUE,
                ),popup=pop_pop) %>%
  leaflet::addLegend(position = "bottomleft", pal = pal, values = c(paste('$',round(min(states_2019$`Average Student Loans`))), 
                                                          paste('$',round(max(states_2019$`Average Student Loans`)))),
            title = "Average Student Loans (Per Student)") %>%   setView(-98.5795, 39.8282, zoom=3)
Some values were outside the color scale and will be treated as NAsf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
Need '+proj=longlat +datum=WGS84'
brewer.pal(n = 8, name = "RdYlGn")
leaflet(sc_time_2019_selective) %>% addTiles('http://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}.png') %>%
  addCircles(col = ~pal1(sc_time_2019_selective$uni_rank),
             radius = ~DEBT_MDN,
             popup = content,
             fillOpacity = 0.7) %>%
  leaflet::addLegend(position = "bottomleft",pal = pal1, values = sc_time_2019_selective$uni_rank,
            title = "Average Student Loans (Per Student)")
Assuming "long" and "lat" are longitude and latitude, respectively
Data contains 931 rows with either missing or invalid lat/lon values and will be ignored

The code chunk below has not been touched and includes Shiny code for later use.

ui <- fluidPage(
    sidebarLayout(
        sidebarPanel(
            p
        ),
        
        mainPanel(
            width = 9,
            h2("Link to a saved sample.html"),
            p("The url is https://johndoe.github.io/samples/sample.html"),
            a(h3("Lovely Birds"),
              href = "https://johndoe.github.io/samples/sample.html",
              target = "blank"),
            hr(),
            h2("Text and image example", id = "birds"),
            fluidRow(
                column(
                    7,
                    includeMarkdown("markdown/birds.Rmd")
                ),
                column(
                    5,
                    img(src = "birds.png", width = "100%")
                )
            ),
        )
    )
)
cannot open file 'markdown/birds.Rmd': No such file or directoryError in file(con, "r") : cannot open the connection
---
title: "Visuals_Draft_04_14"
author: "Connie Xu"
date: "4/14/2021"
output: html_notebook
---

```{r setup, include=FALSE}
setwd("~/Dropbox (Business)/Spring 2021/QMSS 5063 - Data Visualization /Group_G_HigherEd/src/visuals/")
knitr::opts_chunk$set(echo = TRUE)
source("ourtheme.R")
```

```{r packages, echo=FALSE, eval=TRUE, warning=FALSE, message=FALSE}
r = getOption("repos")
r["CRAN"] = "http://cran.us.r-project.org"
options(repos = r)
# install.packages (basic)
suppressMessages(library(dplyr))
suppressMessages(library(tidyverse))

# install.packages (reading)
suppressMessages(library(XML))
suppressMessages(library(RCurl))
suppressMessages(library(readr))
suppressMessages(library("readxl"))

# install.packages (themes)
suppressMessages(library(ggthemes))
suppressMessages(library(ggrepel))
suppressMessages(library(RColorBrewer))
suppressMessages(library(viridis))
suppressMessages(library(hrbrthemes))
suppressMessages(library(plotly))


# install.packages (maps)
suppressMessages(library(RgoogleMaps))
suppressMessages(library(ggmap))
suppressMessages(install.packages("maps"))
suppressMessages(install.packages("tmap")) # install the CRAN version
suppressMessages(library(tmap))
suppressMessages(install.packages('rgeos'))

suppressMessages(library(devtools))
# Let's install the development version from Github. Run
devtools::install_github("rstudio/leaflet")
```

```{r import general data, echo=TRUE, eval=TRUE, message=FALSE, warning=FALSE}
# Set the WD as Group_G_HigherEd 
setwd("~/Dropbox (Business)/Spring 2021/QMSS 5063 - Data Visualization /Group_G_HigherEd")

# While our initial College Scorecard only included 2019 initially, I was able to run a simple python script to concatenate and select a small number of relevant columns for our visualization and analysis. Thus, as our first visualizations only unclude 2019, the code below is for re-filtering the concatenated 2010-2019 data back into simply 2019. 

sc_time <- read_csv('src/2010_2019_student_debt.csv') 
sc <- sc_time %>% filter(Year_Ending == 2019)

library(educationdata)
# Test Run with using get_education_data 
# data <- get_education_data(level = "college-university",
#     source = "ipeds",
#     topic = "directory",
#     filters = list(year = 2019))
# data
# Scorecard data - 2019 

## change projection of sc data
sc <- sc %>%
  dplyr::mutate(uni_rank = case_when(
    ADM_RATE < 0.2 ~ 'highly selective/elite',
    ADM_RATE < 0.3 ~ 'more selective',
    ADM_RATE < 0.5 ~ 'selective',
    ADM_RATE < 0.7 ~ 'less selective',
    TRUE ~ 'not selective')) %>% mutate(uni_rank = factor(uni_rank, levels=c('not selective', 'less selective', 'selective', 'more selective', 'highly selective/elite')))
```

## Simple Scattergram 

First we are going to try to present this pattern for different tiers of universities (admission rate as well as debt)
```{r, echo=TRUE, eval=TRUE}
# Remove PrivacySuppressed Records and transform Debt Median into a numeric value - we can also do this on the main sc df
sc$DEBT_MDN[is.na(sc$DEBT_MDN)] <- 0;

brewer.pal(n=10,"PuBuGn")
ShortPuBuGn <- c("#D0D1E6","#A6BDDB","#67A9CF","#3690C0","#02818A")

m <- sc %>% subset(DEBT_MDN !='PrivacySuppressed') %>% transform(DEBT_MDN = as.numeric(DEBT_MDN)) %>% 
              ggplot(., aes(x=ADM_RATE, y=DEBT_MDN,color=uni_rank)) +
  geom_point(pch=21) +
  geom_smooth(color='#EA4F88', se = FALSE) +
  scale_color_manual(values=ShortPuBuGn)+
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.background= element_rect(fill="white")) +
  scale_y_discrete(limits=c(0,10000,20000,30000), labels=c('0','10','20','30')) +
  labs(x='Admissions Rate', y='Median Loan Amount per Student\n(thousands)', 
       title='Student Debt and Admissions Rate',
       color='Selectivity')
m
```

This is a data table showing the breakdown of the university ranks (as I chose to rank them). I will note that from the dt alone wwe are seeing a downward trend in the 'Median Student Loans' column. 
```{r, echo=TRUE, eval=TRUE}
# Create Data Table (Summarized) for 
library('scales')

sc_dt <- sc %>% subset(DEBT_MDN !='PrivacySuppressed') %>% transform(DEBT_MDN = as.numeric(DEBT_MDN)) %>% group_by(uni_rank) %>% mutate(`Number of Universities` = n()) %>% ungroup() %>% mutate(DEBT_MDN_STUDENTS = DEBT_MDN*UGDS) %>% group_by(uni_rank) %>% mutate(`Median Student Loans` = paste('$',round(sum(DEBT_MDN_STUDENTS, na.rm=TRUE)/sum(UGDS, na.rm=TRUE),2))) %>% 
  mutate(`Min Acceptance Rate` = percent(min(ADM_RATE))) %>% mutate(`Max Acceptance Rate` = percent(max(ADM_RATE))) %>% ungroup() %>% 
  group_by(uni_rank,`Median Student Loans`,`Number of Universities`,`Min Acceptance Rate`,`Max Acceptance Rate`) %>% 
  summarize()

install.packages('DT')
library(DT)
table <- datatable(sc_dt,style = "default",filter = 'top',  caption = 'Universities and Selectivity')
table

```

## Scattergram as Violin Plot
```{r, echo=TRUE, eval=TRUE}
m <- sc %>% subset(DEBT_MDN !='PrivacySuppressed') %>% transform(DEBT_MDN = as.numeric(DEBT_MDN)) %>% 
  ggplot(., aes(x=uni_rank, y=DEBT_MDN)) +
  geom_violin(aes(fill=uni_rank,color=uni_rank)) +
  geom_boxplot(width = 0.2)+
  scale_fill_manual(values=ShortPuBuGn) +
  scale_colour_manual(values=ShortPuBuGn) +
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.background= element_rect(fill="white"))+
  scale_y_discrete(limits=c(0,10000,20000,30000), labels=c('0','10','20','30')) +
  scale_x_discrete(labels=c('not\nselective\n(>70%)','less\nselective\n(50%-70%)',
                            'selective\n(30%-50%)','more\nselective\n(20%-30%)','highly\nselective\n(5%-20%)','elite\n(<5%)')) +

  labs(x='Selectivity\n(admission rate thresholds)', y='Median Loan Amount per Student\n(thousands)', 
       title='Selective Schools and Student Debt',
       color='',fill='')
m
```
Showing the previous scattergram specifically as violing plots

```{r, echo=TRUE, eval=TRUE}
ipeds15 <- get_education_data(level = "college-university",
    source = "ipeds",
    topic = "grad-rates-pell",
    filters = list(year = 2015))
ipeds15
```


## Student Debt over Time

```{r, echo=TRUE, eval=TRUE}

#Process the data 
sc_time <- read_csv('2010_2019_student_debt.csv') 

sc_time<- sc_time %>% subset(DEBT_MDN !='PrivacySuppressed') %>% 
  transform(DEBT_MDN = as.numeric(DEBT_MDN)) %>% 
  dplyr::mutate(DEBT_MDN = ifelse(is.na(DEBT_MDN), 0, DEBT_MDN))

```


```{r, echo=TRUE, eval=TRUE}
#Process the data 
sc_time <- read_csv('2009_2019_student_debt.csv') 

sc_time<- sc_time %>% subset(DEBT_MDN !='PrivacySuppressed') %>% 
  transform(DEBT_MDN = as.numeric(DEBT_MDN)) %>% 
  dplyr::mutate(DEBT_MDN = ifelse(is.na(DEBT_MDN), 0, DEBT_MDN)) %>% 
  mutate(DEBT_MDN_STUDENT = DEBT_MDN*UGDS)
sc_time

sum(sc_time$UGDS,na.rm=TRUE)
```

Below is an interactive line graph (2010-2019) which details the trends in student debt over the years. 
```{r, echo=TRUE, eval=TRUE}
# CPI Inflation Rates - Got Average Yearly Inflation Rate for Scaling for Student Debt 
install.packages('quantmod')
library(quantmod)
getSymbols("CPIAUCSL", src='FRED')
avg.cpi <- apply.yearly(CPIAUCSL, mean)
cf <- as.data.frame(avg.cpi/as.numeric(avg.cpi['2009'])) 
cf$Year_Ending <- format(as.Date(row.names(cf), format="%Y-%m-%d"),"%Y")

# Merged for Inflation 
sc_time_df <- sc_time %>% group_by(`Year_Ending`) %>% mutate(`Average Annual Student Debt - National` = sum(DEBT_MDN_STUDENT,na.rm=TRUE)/sum(UGDS,na.rm=TRUE)) %>% ungroup() %>% 
  dplyr::mutate(uni_rank = case_when(
    ADM_RATE < 0.2 ~ 'elite/highly selective',
    ADM_RATE < 0.3 ~ 'more selective',
    ADM_RATE < 0.5 ~ 'selective',
    ADM_RATE < 0.7 ~ 'less selective',
    TRUE ~ 'not selective')) %>%
  mutate(uni_rank = factor(uni_rank, levels=c('not selective', 'less selective', 'selective', 
                                              'more selective', 'elite/highly selective'))) %>%
  group_by(uni_rank,Year_Ending) %>% 
  mutate(`Average Annual Student Debt (by Selectivity)` = sum(DEBT_MDN_STUDENT,na.rm=TRUE)/sum(UGDS,na.rm=TRUE)) %>% 
  ungroup() %>% 
  group_by(`Year_Ending`,`Average Annual Student Debt (by Selectivity)`,
           uni_rank,`Average Annual Student Debt - National`) %>% summarize() %>% 
  merge(cf) %>% 
  mutate(`Adjusted Average Annual Student Debt` = `Average Annual Student Debt (by Selectivity)`/
           CPIAUCSL) %>% 
  mutate(`Adjusted Average Annual Student Debt - Composite` = `Average Annual Student Debt - National`/
           CPIAUCSL)

sc_df <- sc_time_df %>% group_by(`Average Annual Student Debt - National`,`Adjusted Average Annual Student Debt - Composite`,Year_Ending) %>% summarize() %>% mutate(uni_rank='national average') %>% mutate(`Adjusted Average Annual Student Debt`=`Adjusted Average Annual Student Debt - Composite`) %>% dplyr::mutate(`Average Annual Student Debt (by Selectivity)` = `Average Annual Student Debt - National`) %>% merge(cf) %>% select(Year_Ending,`Average Annual Student Debt (by Selectivity)`, uni_rank, `Average Annual Student Debt - National`, CPIAUCSL, `Adjusted Average Annual Student Debt`,`Adjusted Average Annual Student Debt - Composite`)
sc_time_df <- sc_time_df %>% rbind(sc_df) %>% mutate(uni_rank = factor(uni_rank, levels=c('national average','not selective', 'less selective', 'selective', 'more selective', 'elite/highly selective'))) %>% 
  mutate(national = ifelse(uni_rank == 'national average', 'y','n'))
sc_df
sc_time_df


p <- sc_time_df %>% 
  ggplot(.,aes(x=Year_Ending,y=`Adjusted Average Annual Student Debt`, color=uni_rank, group=national)) + 
  geom_point() + geom_line(aes(linetype=national)) + 
  scale_color_manual(values=c('grey',"#D0D1E6","#A6BDDB","#67A9CF","#3690C0","#02818A"))+
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.background= element_rect(fill="white")) +
  scale_x_continuous(breaks = round(seq(min(sc_time$Year_Ending), max(sc_time$Year_Ending), by = 2),1)) +
  labs(x='', y='Median Loan Amount per Student\n(thousands)**', 
       title='Student Debt Has Been Rising Over The Years',
       color='',fill='', caption='**-inflation adjusted')
ggplotly(p)


```

The following is (instead) a bar graph with fewer 'university selectivity' buckets. 
```{r}
sc_time_df <- sc_time %>% group_by(`Year_Ending`) %>% mutate(`Average Annual Student Debt - National` = sum(DEBT_MDN_STUDENT,na.rm=TRUE)/sum(UGDS,na.rm=TRUE)) %>% ungroup() %>% 
  dplyr::mutate(uni_rank = case_when(
    ADM_RATE < 0.2 ~ 'highly selective',
    ADM_RATE < 0.5 ~ 'moderately selective',
    TRUE ~ 'less/not at all selective')) %>%
  mutate(uni_rank = factor(uni_rank, levels=c('less/not at all selective', 'moderately selective', 'highly selective'))) %>%
  group_by(uni_rank,Year_Ending) %>% 
  mutate(`Average Annual Student Debt (by Selectivity)` = sum(DEBT_MDN_STUDENT,na.rm=TRUE)/sum(UGDS,na.rm=TRUE)) %>% 
  ungroup() %>% 
  group_by(`Year_Ending`,`Average Annual Student Debt (by Selectivity)`,
           uni_rank,`Average Annual Student Debt - National`) %>% summarize() %>% 
  merge(cf) %>% 
  mutate(`Adjusted Average Annual Student Debt` = `Average Annual Student Debt (by Selectivity)`/
           CPIAUCSL) %>% 
  mutate(`Adjusted Average Annual Student Debt - Composite` = `Average Annual Student Debt - National`/
           CPIAUCSL)

sc_df <- sc_time_df %>% group_by(`Average Annual Student Debt - National`,`Adjusted Average Annual Student Debt - Composite`,Year_Ending) %>% summarize() %>% mutate(uni_rank='national average') %>% mutate(`Adjusted Average Annual Student Debt`=`Adjusted Average Annual Student Debt - Composite`) %>% dplyr::mutate(`Average Annual Student Debt (by Selectivity)` = `Average Annual Student Debt - National`) %>% merge(cf) %>% select(Year_Ending,`Average Annual Student Debt (by Selectivity)`, uni_rank, `Average Annual Student Debt - National`, CPIAUCSL, `Adjusted Average Annual Student Debt`,`Adjusted Average Annual Student Debt - Composite`)
sc_time_df <- sc_time_df %>% rbind(sc_df) %>% mutate(uni_rank = factor(uni_rank, levels=c('national average','less/not at all selective', 'moderately selective', 'highly selective'))) 

fig1 <- sc_time_df %>% plot_ly(x = ~Year_Ending, y = ~`Adjusted Average Annual Student Debt`, type = 'bar',
  color = ~uni_rank, alpha=0.8, hovertemplate = 'Average Debt/Student (USD): %{y} <extra></extra>',colors='Purples') %>% 
  layout(yaxis = list(
  title = "Average Annual Student Debt per Student\n(Adjusted for Inflation)"))

fig1

```

Chloropleth showing average student debt (this is only 2019 but I have a filter at the beginning that will allow me to incorporate a slider for the year).

```{r Student Debt map - Chloropleth, echo=TRUE, eval=TRUE }
# Additions of States df from Tigris File 
library(tigris)
states <- states(cb = TRUE)

# Can change to sc_time_year 
sc_time_2019 <- sc_time %>% subset(Year_Ending = 2019) %>% 
  group_by(STABBR) %>% mutate(`Average Student Loans`=sum(DEBT_MDN_STUDENT,na.rm=TRUE)/sum(UGDS,na.rm=TRUE))
sc_time_2019_state <- sc_time_2019 %>% group_by(STABBR,`Average Student Loans`) %>% summarize()

# States 
states_2019 <- states %>% 
  inner_join(sc_time_2019_state, by=c(STUSPS='STABBR')) 


library(leaflet.providers)
library(leaflet)
#used 'success' measures. 

pal = colorFactor(ShortPuBuGn, domain = states_2019$`Average Student Loans`)
states_2019
pop_pop <- paste("State:",states_2019$NAME,"<br/>",
                 "Average Student Loans","<br/>",
                 "of Schools Located in State:",paste('$',round(states_2019$`Average Student Loans`)))

leaflet(states_2019) %>% addProviderTiles("CartoDB.Positron") %>%
  addPolygons(fillColor = ~pal(states_2019$`Average Student Loans`),
              color = "white",
              weight = 0.5,
              fillOpacity = 0.7,  
              highlight = highlightOptions(
                weight = 5,
                color = "#666",
                fillOpacity = 0.7,
                bringToFront = TRUE,
                ),popup=pop_pop) %>%
  leaflet::addLegend(position = "bottomleft", pal = pal, values = c(paste('$',round(min(states_2019$`Average Student Loans`))), 
                                                          paste('$',round(max(states_2019$`Average Student Loans`)))),
            title = "Average Student Loans (Per Student)") %>%   setView(-98.5795, 39.8282, zoom=3)
?addLegend
```

```{r}
brewer.pal(n = 8, name = "RdYlGn")
```

```{r Student Debt Map - points, echo=TRUE, eval=TRUE }
# last edit to sc_time_2019 in chunk above for chloropleth
sc_time_2019_selective <- sc_time_2019 %>% dplyr::rename(lat = LATITUDE) %>% dplyr::rename(long = LONGITUDE) %>% 
  dplyr::rename(state = STABBR) %>% filter(ADM_RATE < 0.3) %>% 
  dplyr::mutate(uni_rank = case_when(
    ADM_RATE < 0.05 ~ 'elite',
    ADM_RATE < 0.2 ~ 'highly selective',
    TRUE ~ 'selective'))
sc_time_2019_selective <- sc_time_2019_selective %>% subset(DEBT_MDN !='PrivacySuppressed') %>% 
  transform(DEBT_MDN = as.numeric(DEBT_MDN)) %>% 
  dplyr::mutate(DEBT_MDN = ifelse(is.na(DEBT_MDN), 0, DEBT_MDN)) 



pal1 = colorFactor(ShortPuBuGn, domain = sc_time_2019_selective$`uni_rank`,reverse=TRUE)

#set popups 
content <- paste("School",sc_time_2019_selective$INSTNM,"<br/>",
                 "Number of Undergrads:",sc_time_2019_selective$UGDS,"<br/>",
                 "Selectivity:", sc_time_2019_selective$uni_rank,"<br/>",
                 "Median Debt:",paste('$',round(sc_time_2019_selective$DEBT_MDN,2)),"<br/>")
sc_time_2019_selective
leaflet(sc_time_2019_selective) %>% addTiles('http://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}.png') %>%
  addCircles(col = ~pal1(sc_time_2019_selective$uni_rank),
             radius = ~DEBT_MDN,
             popup = content,
             fillOpacity = 0.7) %>%
  leaflet::addLegend(position = "bottomleft",pal = pal1, values = sc_time_2019_selective$uni_rank,
            title = "Average Student Loans (Per Student)")
?addLegend
```


The code chunk below has not been touched and includes Shiny code for later use. 
```{r}
library(reshape2)
library(shiny)
ui <- fluidPage(
    sidebarLayout(
        sidebarPanel(
            p
        ),
        
        mainPanel(
            width = 9,
            h2("Link to a saved sample.html"),
            p("The url is https://johndoe.github.io/samples/sample.html"),
            a(h3("Lovely Birds"),
              href = "https://johndoe.github.io/samples/sample.html",
              target = "blank"),
            hr(),
            h2("Text and image example", id = "birds"),
            fluidRow(
                column(
                    7,
                    includeMarkdown("markdown/birds.Rmd")
                ),
                column(
                    5,
                    img(src = "birds.png", width = "100%")
                )
            ),
        )
    )
)


```



